‫Welcome back.
‫So while you were gone in between the last video and this one, I've kind of tidied up this data dictionary
‫here, so it looks a bit better now.
‫And don't worry, you'll have a copy of this in the resources section.
‫So you're not going to be missing out on anything.
‫But just where we left off, we talked about features, which is step four in the machine learning pipeline
‫that we're using or the machine learning modeling pipeline that we're using.
‫And this is where you get different information about each of the features in your data.
‫You can do this by doing your own researchers, researchers by doing your own research, such as looking
‫at the links above.
‫That's what I did here to kind of tidy this up reading between this information here.
‫So attribute information as well as UCI data set web page.
‫If you go through here, here are all 76 attributes, but realistically we only used 14, so only 14
‫attributes used.
‫And that's what this data dictionary is.
‫So basically a data dictionary just tells you about the data that you're working on.
‫Wonderful.
‫And so now where we up to if we check back?
‫We've worked through one, two, three, four.
‫But have we really?
‫So we've we've just kind of done it in thought.
‫So now it's time to apply the tools that we've been going through to this section, right?
‫So this data analysis section.
‫So let's do that.
‫Let's get our tools ready.
‫So create a heading here.
‫Preparing the tools.
‫We're going to use pandas, map, plot lib and numpy for data analysis and manipulation.
‫Wonderful.
‫And so at the start of every notebook, what you might do is import.
‫This is what you'll probably see out in the wild is a big chunk or one big Jupyter cell.
‫Importing all different tools at once.
‫You don't necessarily have to do it this way, but it's kind of industry standard to do it this way.
‫So let's see what that would look like.
‫Import all the tools we need.
‫So what we might do is make little subheadings here.
‫So that way, when we're working through this step by step process, we've got all the tools that we
‫need.
‫And now, again, a project might not start out like this.
‫I'm only doing it this way because this is what you'll see all of this sort of finished project.
‫What you're more regularly do is kind of how we've been working with it in the past is just import a
‫tool as you need it.
‫So when you see this big block of code, then I want about to type with all the imports and all different
‫tools and whatnot.
‫You might look at it and go, Holy goodness, I'm not sure what's going on with everything there, but
‫that's okay.
‫What you'll do is because you've got the framework now you've got this and we've seen different ways
‫that we can apply different tooling.
‫You'll be pretty familiar with all the functionality that we're going through here, and if not, that's
‫perfectly okay.
‫You know where to look and you know where to get help.
‫So let's do this.
‫We'll go regular EDA and plotting libraries.
‫Here's where we're going to import NumPy.
‫EDA stands for exploratory data analysis, by the way.
‫Got to learn not to talk in acronyms.
‫We coders like to shorten everything.
‫So import numpy as NP import pandas as PD import map plot lib dot py plot as plt at plt come on down
‫import seabourn as psns.
‫So Seabourn remember is just that plotting library build on top of map plot lib map plot lib is the
‫base plotting library that we're going to be using.
‫So I'm going to do map plot lib in line because.
‫We want our plot to appear inside the notebook.
‫So this is what this little magic function does with that percentage sign there.
‫And then we want to import models from cyclone or cyclone, also known as cyclone.
‫You're about to see that in a second.
‫So from SRC learn we want a linear model import logistic regression.
‫Now if you're wondering where these models came from, remember we've got cyclone model map.
‫Choosing the right estimate.
‫Wonderful.
‫So we followed this along in a previous section.
‫We're working on a classification problem, so we're only going to be importing classification models.
‫So from ESC Learn DOT neighbors, we're also going to import another model here.
‫So can neighbors classifier.
‫And you remember this one ensemble?
‫You might not have seen the first two, but that's okay.
‫We'll talk about them when when it comes to it.
‫Random forest classifier.
‫We have seen that one.
‫And then we're going to go model evaluations.
‫We're going to go from cyclone dot model selection, import train test split.
‫This is going to help us split our data into training and test sets.
‫We'll also import cross vowel score from here.
‫Now, if you're wondering how I know all these, it's because I've had some practice.
‫And if you're looking at this going, Holy goodness, there's a lot of things to remember to import,
‫and you're like, Oh, that's what I'm saying.
‫I'm agreeing with you.
‫But machine learning is a very broad field, and there's a lot of different ways to do similar things.
‫But after some hands on practice, like what we're going through now, you'll start to get a bit more
‫familiar.
‫And again, it's no rush.
‫I still have to look up things.
‫So I've done these on separate lines, these two.
‫I could have done them on the same line because they're from the same module.
‫But just to keep it a little bit tidy.
‫A little bit polyphonic.
‫We want to go hear from Scaloni.
‫Matrix Import Confusion.
‫Matrix Classification Report.
‫Well, they may be familiar from the evaluation section of the cyclone section.
‫We go here import.
‫We want to get precision score because we're thinking in our head, this is a classification problem.
‫What metrics can we use to evaluate classification models?
‫F one score from scalar and matrix import plot rc co because we want to rock curve.
‫Well, you might be looking at that going, Daniel, that is a lot of stuff and it truly it is.
‫This is 21 lines of imports minus a few comments, but this is the tools that we're going to need.
‫So if we come back here, we might still even need more.
‫I might have missed some, but this is kind of what you'll see at the start of a notebook is because
‫once we've set it up, we want to approach these and we want to minimize our time between experiments.
‫So that's why we're getting all our tooling ready right at the top so that we can get hands on as soon
‫as possible.
‫So we've got pandas, we've got map, plot lab.
‫We've got an umpire we're working with in a Jupyter notebook.
‫In our condor environment, we've got cyclone, and now we're going to work through the data analysis
‫section.
‫So that means basically importing the data, having a look at it, and trying to become ourselves a
‫subject matter expert, which is just someone who knows about the data, like a fancy word for someone
‫who knows about the data.
‫That's what we're trying to do in these three steps here.
‫So without further ado, let's hit shift and end to import our tools.
‫Fingers crossed.
‫If we've set up our environment to work, they showed it all import correctly.
‫What are we getting here?
‫Something wrong?
‫Runtime error MP Q Font size change may indicate binary incompatibility expected 192 from C.A. got to
‫16 from PI Object.
‫I've actually never seen that error before.
‫So this is what you come across, right?
‫These type of things.
‫Even after you've had some experience, you'll come across these things, this warning.
‫So we're just waiting on to make sure that this cell runs fully.
‫It's already given us a warning.
‫Oh, we're getting another warning.
‫Here we go.
‫MP that you func now we might let this cell run out as in finish, because it's going to take a little
‫while.
‫Unrecognized arguments.
‫Oh, maybe I can't put this.
‫This needs to go above here.
‫There we go.
‫Will this import?
‫Yes, it should.
‫And now we're getting no warnings, huh?
‫That's interesting.
‫Well, that's the surprises that you get from day to day running, working with different machine learning
‫code and different code in general.
‫It sometimes it does things that you don't really expect.
‫And so that warning has gone away.
‫If it does come back in the future, we'll probably deal with it then.
‫But for now, we'll assume that we've got our tools ready.
‫Now we can start applying them.